International Journal of Remote Sensing

Hui, Fengming; Xu, Bing; Huang, Huabing; Yu, Qian; Gong, Peng

doi: 10.1080/01431160802060912pmid: N/A

Poyang Lake is a seasonal lake, exchanging water with the lower branch of the Yangtze River. During the spring and summer flooding season it inundates a large area while in the winter it shrinks considerably, creating a large tract of marshland for wild migratory birds. A better knowledge of the water coverage duration and the beginning and ending dates for the vast range of marshlands surrounding the lake is important for the measurement, modelling and management of marshland ecosystems. In addition, the abundance of a special type of snail (Oncomelania hupensis), the intermediate host of parasite schistosome (Schistosoma japonicum) in this region, is also heavily dependent on the water coverage information. However, there is no accurate digital elevation model (DEM) for the lake bottom and the inundated marshland, nor is there sufficient water level information over this area. In this study, we assess the feasibility of the use of multitemporal Landsat images for mapping the spatial‐temporal change of Poyang Lake water body and the temporal process of water inundation of marshlands. Eight cloud‐free Landsat Thematic Mapper images taken during a period of one year were used in this study. We used the normalized difference water index (NDWI) and the modified normalized difference water index (MNDWI) methods to map water bodies. We then examined the annual spatial‐temporal change of the Poyang Lake water body. Finally we attempted to obtain the duration of water inundation of marshlands based on the temporal sequence of water extent determined from the Landsat images. The results showed that although the images can be used to capture the snapshots of water coverage in this area, they are insufficient to provide accurate estimation of the spatial‐temporal process of water inundation over the marshlands through linear interpolation.

Oesch, D.; Jaquet, J.‐M.; Klaus, R.; Schenker, P.

doi: 10.1080/01431160802132786pmid: N/A

The applicability of satellite imagery products from different sensors (AVHRR‐derived multi‐channel sea surface temperature (MCSST), MODIS sea surface temperature (SST) products 5‐Min L2 Swath 1 km and Landsat TM band 6 thermal signature) for the comprehensive monitoring of temperature and its temporal patterns over a large lake is tested in this study. The coverage of cloud‐free satellite data for Lake Geneva is reported throughout a year and, more specifically, during a 13 day period in summer 2003. In a second step, we demonstrate the feasibility of the AVHRR/MODIS imagery to discern day and night temperature patterns, by generating day and night climatologies and various spatial statistics over the 13 day period. The different day and night surface thermal patterns observed by satellite imagery could be linked to the thermal structure existing in deeper water using the concept of the diurnal decoupled layer. The forcing of the persistent patterns, two warm cores divided by a saddle‐shaped cold anomaly, is explained by wind periodicity and insolation conditions. The patterns can be matched to features postulated by findings of different limnologists in the past. Other surface temperature related phenomena such as water upwelling and downwelling and the occurrence of plumes are related to meteorological and hydrological events. The lakewide average lake surface water temperature (LSWT) trends for day and night during the study period are roughly parallel. A sudden loss of stored heat can be explained by episodes of long fetch, synoptic wind (bise) that interrupted the predominant breeze regime.

Henderson, Floyd M.; Lewis, Anthony J.

doi: 10.1080/01431160801958405pmid: N/A

Periodically, reviews of our knowledge of radar–wetland relationships and detection parameters have been provided by various authors. Since the publication of these works, additional research has been completed. Five major remote sensing journals spanning the years 1965–2007 formed the basis of this review. The vast majority of significant material found its way into these mainstream journals in one aspect or another. A short history of Synthetic Aperture Radar (SAR)–wetlands discovery based on earlier reviews is followed by an update on radar‐related wetland research. Although some trends emerged with regard to which wavelengths or polarizations to use, there was variation in optimum season/time of year and selection of multitemporal imagery. What is evident throughout the recent literature is that multidimensional radar data sets are attaining an accepted role in operational situations needing information on wetland presence, extent and conditions.

Waisurasingha, C.; Aniya, M.; Hirano, A.; Sommut, W.

doi: 10.1080/01431160802029669pmid: N/A

In Thailand, flooding due to seasonal monsoon conditions frequently destroys a substantial amount of rice production, the most important agricultural activity of the country. Taking the 2001 monsoon flooding that hit the Lower Chi River Basin as an example, we developed a new method for accurately assessing damage to flood‐affected paddies. A RADARSAT‐1 image acquired during peak flooding was combined with a 30‐m digital elevation model (DEM) to develop a ‘flood‐level‐determination’ algorithm for estimating floodwater depth. Based on the elongation capability of the rice varieties, a water depth of 80 cm was used to separate ‘non‐damaged’ from ‘damaged’ paddy areas, indicating that about 60% of the paddy fields in the flooded areas were non‐damaged paddies. To minimize the loss of rice and maximize farmers' incomes, a map of rice varieties appropriate for the damaged paddy areas was produced, combining the flood‐affected paddy map with the flood frequency map. Our results demonstrate the potential of using single‐date RADARSAT‐1 data and a DEM to provide accurate and economic means of assessing flood damage to rice fields that can be used to improve rice production.

Tapiador, F. J.

doi: 10.1080/01431160802029677pmid: N/A

A cloud motion winds (CMW) method is presented for improving quantitative rainfall estimation advection schemes that use both infrared (IR) and passive microwave (PMW) satellite data. Advection schemes are used to provide quantitative rainfall estimates by combining more direct PMW rainfall estimates with more frequent IR cloud top temperature measures using a two‐step technique: (1) PMW estimates are transported along CMW trajectories calculated with an advection scheme at subpixel resolution; and (2) PMW estimates are calibrated using the IR gradient along those trajectories. These schemes outperform traditional methods of satellite rainfall estimation but no clear physical basis for the procedure has yet been described. Here, the physical basis for the image processing techniques used in advection techniques is described. It is shown that geostationary satellite‐derived CMW from IR sensors can be modelled in terms of fluid dynamics using Navier–Stokes equations. This approach allows for modelling the problem as equivalent to the flow of a brightness temperature field, also providing subpixel resolution and unlimited rotation/deformation possibilities. The method is illustrated with rainfall estimates from a numerical weather prediction (NWP) model and with 3‐hourly PMW products as simulation data, obtaining consistent results.

Jain, Sanjay K.; Goswami, Ajanta; Saraf, A. K.

doi: 10.1080/01431160801908129pmid: N/A

Snow cover information is an essential parameter for a wide variety of scientific studies and management applications, especially in snowmelt runoff modelling. Until now NOAA and IRS data were widely and effectively used for snow‐covered area (SCA) estimation in several Himalayan basins. The suit of snow cover products produced from MODIS data had not previously been used in SCA estimation and snowmelt runoff modelling in any Himalayan basin. The present study was conducted with the aim of assessing the accuracy of MODIS, NOAA and IRS data in snow cover mapping under Himalayan conditions. The total SCA was estimated using these three datasets for 15 dates spread over 4 years. The results were compared with ground‐based estimation of snow cover. A good agreement was observed between satellite‐based estimation and ground‐based estimation. The influence of aspect in SCA estimation was analysed for the three satellite datasets and it was observed that MODIS produced better results. Snow mapping accuracy with respect to elevation was tested and it was observed that at higher elevation MODIS sensed more snow and proved better at mapping snow under mountain shadow conditions. At lower elevation, IRS proved better in mapping patchy snow cover due to higher spatial resolution. The temporal resolution of MODIS and NOAA data is better than IRS data, which means that the chances of getting cloud‐free scenes is higher. In addition, MODIS has an automated snow‐mapping algorithm, which reduces the time and errors incorporated during processing satellite data manually. Considering all these factors, it was concluded that MODIS data could be effectively used for SCA estimation under Himalayan conditions, which is a vital parameter for snowmelt runoff estimation.

Ghoneim, E.

doi: 10.1080/01431160801932517pmid: N/A

Due to the increase in urban and agricultural activities in arid regions, the exploration of new locations of possible groundwater discharge and accumulation is required to augment the limited water resources. In order to locate such discharge areas, it is necessary first to identify zones of high recharge potentials. In such an arid region, like the northern United Arab Emirates (UAE), one of the ways to predict areas of potential groundwater recharge is by understanding the hydrological response of its drainage basins to rainfall events. Due to the scarcity of basic hydrological data, a hydrological model driven mainly by information on the physiographic characteristics, drainage network properties (generated from DEM), and surface cover distribution (generated from satellite images) was used to comprehend the dynamics of surface runoff through hydrographs, and hence water loss in the study area. Results show that the northern UAE is drained by 48 drainage basins emerging from the Oman Mountains. Two‐thirds of these basins drain easterly toward the Gulf of Oman, and one‐third drain westerly toward the Arabian Gulf. These basins are found to be structurally controlled by three major fault trends, which are the NE trend (Dibba zone), NW trend (Ham Zone), and WNW trend (Hatta zone). The hydrological response of a basin is correlated with its morphological characteristics. Based on these characteristics, and through the application of a cluster analysis, it was feasible to classify the largest basins in the region into four groundwater potentiality groups in accordance with the magnitude of their peak discharges. From this study, it became evident that the downstream area of the two major basins of Ar‐Rafiah and Limhah, and their vicinities are the most probable sites for groundwater accumulation. The drainage systems of these two basins, especially those controlled by major fault lines, play a vital role in transmitting surface–subsurface rainwater from the Oman Mountains, the recharge zone, into the western desert plain, the discharge zone, where freshwater accumulates underground. The study also revealed that a large volume of groundwater is dissipated into the sea along the eastern coast. A detailed examination of MODIS thermal data supports this by revealing cool surface anomalies issuing from the mountain range toward both the western desert plain and the Gulf of Oman following major rainfall events. Thus, the technique used facilitates the prediction of new locations of optimum groundwater resources in the northern UAE. Such a technique could be adopted, with appropriate modifications, elsewhere in arid regions, where groundwater is restricted and subject to greater complexity.

Ghude, Sachin D.; Singh, Sachchidanand; Kulkarni, P. S.; Kumar, A.; Jain, S. L.; Singh, R.; Arya, B. C.; Shahnawaz,

doi: 10.1080/01431160802108505pmid: N/A

Measurements of direct UV irradiances (using a MICROTOPS II Sunphotometer) carried out from a high‐latitude site, Antarctica are presented. The instantaneous irradiances at 305±0.9, 312±0.9 and 320±1.0 nm during a no‐ozone‐hole (13 December 2004) and an ozone‐hole (4 October 2004) period have been observed to be about 0.031, 0.150 and 0.299 W m−2 and 0.010, 0.049 and 0.102 W m−2, respectively at local noontime. The observations of the direct UV irradiances at 305±0.9, 312±0.9 and 320±1.0 nm are compared with tropospheric ultraviolet visible (TUV) radiation transfer model calculations. The model estimate shows that, during the ozone‐hole period, a loss of ozone of the order of 44% leads to an increase in irradiance of the order of 410%, 90% and 25% at 305±0.9, 312±0.9 and 320±1.0 nm, respectively. The relationship between change in UV irradiance due to a change in column ozone is obtained using a TUV model and irradiances thus estimated from this relationship are found to compare well with the observed irradiances.

Huang, X.; Zhang, L.; Li, P.

doi: 10.1080/01431160802139922pmid: N/A

A novel methodology based on multiscale spectral and spatial information fusion using wavelet transform is proposed in order to classify very high resolution (VHR) satellite imagery. Conventional wavelet‐based feature extraction methods employ single windows of a fixed size, which are not satisfactory as the VHR imagery contains complex and multiscale objects. In this paper, spectral and spatial features are extracted based on a set of concentric windows around a central pixel in order to integrate the information across different windows/scales. The proposed method is made up of three blocks: (1) the conventional wavelet‐based feature extraction methods are extended from single band processing to multispectral bands, and from single window to multi‐windows, (2) two multiscale fusion algorithms are proposed to exploit the multiscale spectral and spatial information and (3) a support vector machine (SVM), a relatively new method of machine learning, is used to classify the multiscale spectral–spatial feature sets. The proposed classification method is evaluated on two VHR datasets and the results show that the multiscale approach can improve the classification accuracy in homogeneous areas while simultaneously preserving accuracy in edge regions.

Liu, J. G.; Yan, H.

doi: 10.1080/01431160802144195pmid: N/A

With singular value decomposition (SVD) and robust 2‐dimensional fitting phase correlation algorithms, it is possible to achieve pixel‐to‐pixel image co‐registration at sub‐pixel accuracy via local feature matching. However, the method often fails in featureless and low correlation areas making it not robust for co‐registration of images with considerable spectral differences and large featureless ground objects. A median shift propagation (MSP) technique is proposed to eliminate the problem, in a phase correlation and Normalized Cross‐Correlation (NCC) combined approach. The experiment results using images from different sensor platforms and spectral bands indicate that the new method is very robust to featureless and low correlation areas and can achieve very accurate pixel‐to‐pixel image co‐registration with good tolerance of spectral and spatial differences between images. The method will significantly improve change detection in various remote sensing applications.